Machine-learning the string landscape

He, Y. (2017). Machine-learning the string landscape. Physics Letters B, 774, pp. 564-568. doi: 10.1016/j.physletb.2017.10.024

Text - Published Version
Available under License Creative Commons Attribution.

Download (344kB) | Preview


We propose a paradigm to apply machine learning various databases which have emerged in the study of the string landscape. In particular, we establish neural networks as both classifiers and predictors and train them with a host of available data ranging from Calabi–Yau manifolds and vector bundles, to quiver representations for gauge theories, using a novel framework of recasting geometrical and physical data as pixelated images. We find that even a relatively simple neural network can learn many significant quantities to astounding accuracy in a matter of minutes and can also predict hithertofore unencountered results, whereby rendering the paradigm a valuable tool in physics as well as pure mathematics.

Item Type: Article
Divisions: School of Engineering & Mathematical Sciences > Department of Mathematical Science

Actions (login required)

View Item View Item


Downloads per month over past year

View more statistics